Add SetFit model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+base_model: BAAI/bge-base-en-v1.5
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 'Reasoning for Good:
+
+    1. **Context Grounding**: The answer is supported by the document, which clearly
+    indicates that Forbes began reporting on Beyoncé''s earnings in 2008.
+
+    2. **Relevance**: The answer specifically addresses the question asked about who
+    began reporting Beyoncé''s annual earnings starting in 2008.
+
+    3. **Conciseness**: The answer is brief and directly to the point, without including
+    extraneous information.
+
+
+    Reasoning for Bad:
+
+    1. **Context Grounding**: While the statement is accurate, it introduces the aspect
+    of a "widespread misconception" about Times Magazine, which is not mentioned in
+    the provided document.
+
+    2. **Relevance**: The mention of Times Magazine might be seen as deviating slightly
+    from the question, which just asked about the first entity to begin reporting
+    Beyoncé''s earnings.
+
+    3. **Conciseness**: The answer could have been more concise by focusing solely
+    on Forbes without mentioning the misconception about Times Magazine.
+
+
+    Final result: Bad'
+- text: 'The answer provided is:
+
+
+    "The average student at Notre Dame travels more than 750 miles to study there."
+
+
+    Reasoning:
+
+
+    **Good points:**
+
+    1. **Context Grounding**: The answer is supported by information present in the
+    document, which states, "the average student traveled more than 750 miles to Notre
+    Dame".
+
+    2. **Relevance**: The answer directly addresses the specific question asking about
+    the number of miles the average student travels to study at Notre Dame.
+
+    3. **Conciseness**: The answer is clear and to the point without any unnecessary
+    information.
+
+
+    **Bad points:**
+
+    - There are no bad points in this case as the answer aligns perfectly with all
+    the evaluation criteria.
+
+
+    Final Result: **Good**'
+- text: 'Reasoning why the answer may be good:
+
+    - The answer correctly identifies Mick LaSalle as the writer for the San Francisco
+    Chronicle.
+
+    - The answer states that Mick LaSalle awarded "Spectre" a perfect score, which
+    is supported by the document.
+
+
+    Reasoning why the answer may be bad:
+
+    - The answer is concise and to the point, fulfilling the criteria for conciseness
+    and relevance.
+
+    - The document provided confirms that Mick LaSalle gave "Spectre" a perfect score
+    of 100.
+
+    - There is no deviation into unrelated topics, maintaining focus on the question
+    asked.
+
+
+    Final result: Good'
+- text: "Reasoning: \n\nWhy the answer may be good:\n- The answer directly addresses\
+    \ the specific question asked, \"What New York borough contains the highest population\
+    \ of Asian-Americans?\" \n- It is well-supported by the given document, which\
+    \ states, \"The New York City borough of Queens is home to the state's largest\
+    \ Asian American population.\"\n- The answer is clear and concise without unnecessary\
+    \ information.\n\nWhy the answer may be bad:\n- There are no significant reasons\
+    \ to consider the answer bad based on the criteria provided. \n\nFinal Result:\
+    \ \n\nGood"
+- text: "The answer may be good:\n- The information provided in the answer is supported\
+    \ by the document. \n\nThe answer may be bad:\n- The answer does not address the\
+    \ specific question asked which pertains to the year that Doctorate degrees were\
+    \ first granted at Notre Dame.\n- It deviates into unrelated information about\
+    \ the opening of a theology library, which is irrelevant to the question.\n\n\
+    Final result: Bad"
+inference: true
+model-index:
+- name: SetFit with BAAI/bge-base-en-v1.5
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8360655737704918
+      name: Accuracy
+---
+
+# SetFit with BAAI/bge-base-en-v1.5
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 2 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1     | <ul><li>'Reasoning why the answer may be good:\n1. **Context Grounding**: The answer is directly supported by the document, which explicitly states that "With almost every line of his epic Punica Silius references Virgil."\n2. **Relevance**: The answer specifically addresses the question asked by identifying the title of Silius Italicus\' epic where Virgil is frequently referenced.\n3. **Conciseness**: The answer is short, clear, and to the point, providing just the necessary information without any extraneous details.\n\nReasoning why the answer may be bad:\n- There is no evidence of deviation or lack of support from the provided document, the relevance is clearly maintained, and the answer concisely addresses the question.\n\nFinal Result: Good'</li><li>'Good'</li><li>'Reasoning:\n\nWhy the answer may be good:\n1. Context Grounding: The answer mentions "3,000 police," which correlates with the figure provided in the document regarding the number of French police that protected the Olympic torch relay.\n2. Relevance: The answer directly addresses the question, which asks about the number of police protecting the torch in France.\n3. Conciseness: The answer is brief and to the point without adding any unnecessary information.\n\nWhy the answer may be bad:\nThere is no evident issue with context grounding, relevance, or conciseness in the answer provided.\n\nFinal result: Good'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+| 0     | <ul><li>"**Reasoning Why the Answer May Be Good:**\n- The answer correctly identifies a person associated with vice-presidential and presidential roles at Notre Dame, although it attributes the wrong timeframe for the vice-presidency.\n\n**Reasoning Why the Answer May Be Bad:**\n- The document specifically mentions that John Francis O'Hara became vice-president in 1933, not James Edward O'Hara, indicating the answer is not well-supported by the provided document.\n- The answer provides incorrect and irrelevant information that does not address the specific question asked.\n- The question asked for the vice-president elected in 1933, and the answer incorrectly identifies the year 1934.\n\n**Final Result:**\nBad"</li><li>"Reasoning:\n1. **Context Grounding**: The document does provide the necessary information about the gross earnings of Beyoncé's second world tour. Therefore, the answer is well-supported by the document.\n2. **Relevance**: The answer directly responds to the specific question asked about the gross earnings of Beyoncé during her second world tour in 2009.\n3. **Conciseness**: The answer is concise and sticks to the point, providing the exact figure and relevant context about the record without additional unnecessary information.\n\nFinal Result: Good"</li><li>"Reasoning:\n\nWhy the answer may be good:\n- The answer specifies a borough of New York, which is relevant to the question.\n- It provides a specific claim about the population distribution of Asian-Americans within New York City boroughs.\n\nWhy the answer may be bad:\n- The provided document explicitly states that Queens is home to the state's largest Asian-American population, not Manhattan.\n- The answer does not align with the key information from the document, thus failing the test of context grounding.\n\nFinal Result: Bad"</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8361   |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("Netta1994/setfit_baai_squad_gpt-4o_improved-cot-instructions_two_reasoning_only_reasoning_17267")
+# Run inference
+preds = model("The answer may be good:
+- The information provided in the answer is supported by the document. 
+
+The answer may be bad:
+- The answer does not address the specific question asked which pertains to the year that Doctorate degrees were first granted at Notre Dame.
+- It deviates into unrelated information about the opening of a theology library, which is irrelevant to the question.
+
+Final result: Bad")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median  | Max |
+|:-------------|:----|:--------|:----|
+| Word count   | 1   | 91.8596 | 275 |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 27                    |
+| 1     | 30                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (5, 5)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- body_learning_rate: (2e-05, 2e-05)
+- head_learning_rate: 2e-05
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0070 | 1    | 0.1646        | -               |
+| 0.3497 | 50   | 0.2544        | -               |
+| 0.6993 | 100  | 0.1157        | -               |
+| 1.0490 | 150  | 0.0294        | -               |
+| 1.3986 | 200  | 0.0037        | -               |
+| 1.7483 | 250  | 0.0025        | -               |
+| 2.0979 | 300  | 0.0023        | -               |
+| 2.4476 | 350  | 0.002         | -               |
+| 2.7972 | 400  | 0.0018        | -               |
+| 3.1469 | 450  | 0.0017        | -               |
+| 3.4965 | 500  | 0.0016        | -               |
+| 3.8462 | 550  | 0.0017        | -               |
+| 4.1958 | 600  | 0.0016        | -               |
+| 4.5455 | 650  | 0.0015        | -               |
+| 4.8951 | 700  | 0.0016        | -               |
+
+### Framework Versions
+- Python: 3.10.14
+- SetFit: 1.1.0
+- Sentence Transformers: 3.1.0
+- Transformers: 4.44.0
+- PyTorch: 2.4.1+cu121
+- Datasets: 2.19.2
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "BAAI/bge-base-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.1.0",
+    "transformers": "4.44.0",
+    "pytorch": "2.4.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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+oid sha256:5cfc7851d7528eb096f1de3d22673a89f066049c9384a5575f0bd84ae3ecd875
+size 437951328

model_head.pkl

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+size 7007

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
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+    "lstrip": false,
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+    "single_word": false
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+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
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+      "lstrip": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}